{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b86e5578",
   "metadata": {},
   "source": [
    "```{try_on_binder}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c655123",
   "metadata": {
    "load": "myst_code_init.py",
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pymor.discretizers.builtin.gui.jupyter extension is already loaded. To reload it, use:\n",
      "  %reload_ext pymor.discretizers.builtin.gui.jupyter\n"
     ]
    }
   ],
   "source": [
    "from IPython import get_ipython\n",
    "ip = get_ipython()\n",
    "if ip is not None:\n",
    "    ip.run_line_magic('load_ext', 'pymor.discretizers.builtin.gui.jupyter')\n",
    "    ip.run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module='torch')\n",
    "import pymor.tools.random\n",
    "pymor.tools.random._default_random_state = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a72f1211",
   "metadata": {},
   "source": [
    "# Tutorial: Model order reduction with artificial neural networks\n",
    "\n",
    "Recent success of artificial neural networks led to the development of several\n",
    "methods for model order reduction using neural networks. pyMOR provides the\n",
    "functionality for a simple approach developed by Hesthaven and Ubbiali in {cite}`HU18`.\n",
    "For training and evaluation of the neural networks, [PyTorch](<https://pytorch.org>) is used.\n",
    "\n",
    "In this tutorial we will learn about feedforward neural networks, the basic\n",
    "idea of the approach by Hesthaven et al., and how to use it in pyMOR.\n",
    "\n",
    "## Feedforward neural networks\n",
    "\n",
    "We aim at approximating a mapping {math}`h\\colon\\mathcal{P}\\rightarrow Y`\n",
    "between some input space {math}`\\mathcal{P}\\subset\\mathbb{R}^p` (in our case the\n",
    "parameter space) and an output space {math}`Y\\subset\\mathbb{R}^m` (in our case the\n",
    "reduced space), given a set {math}`S=\\{(\\mu_i,h(\\mu_i))\\in\\mathcal{P}\\times Y: i=1,\\dots,N\\}`\n",
    "of samples, by means of an artificial neural network. In this context, neural\n",
    "networks serve as a special class of functions that are able to \"learn\" the\n",
    "underlying structure of the sample set {math}`S` by adjusting their weights.\n",
    "More precisely, feedforward neural networks consist of several layers, each\n",
    "comprising a set of neurons that are connected to neurons in adjacent layers.\n",
    "A so called \"weight\" is assigned to each of those connections. The weights in\n",
    "the neural network can be adjusted while fitting the neural network to the\n",
    "given sample set. For a given input {math}`\\mu\\in\\mathcal{P}`, the weights between the\n",
    "input layer and the first hidden layer (the one after the input layer) are\n",
    "multiplied with the respective values in {math}`\\mu` and summed up. Subsequently,\n",
    "a so called \"bias\" (also adjustable during training) is added and the result is\n",
    "assigned to the corresponding neuron in the first hidden layer. Before passing\n",
    "those values to the following layer, a (non-linear) activation function\n",
    "{math}`\\rho\\colon\\mathbb{R}\\rightarrow\\mathbb{R}` is applied. If {math}`\\rho`\n",
    "is linear, the function implemented by the neural network is affine, since\n",
    "solely affine operations were performed. Hence, one usually chooses a\n",
    "non-linear activation function to introduce non-linearity in the neural network\n",
    "and thus increase its approximation capability. In some sense, the input\n",
    "{math}`\\mu` is passed through the neural network, affine-linearly combined with the\n",
    "other inputs and non-linearly transformed. These steps are repeated in several\n",
    "layers.\n",
    "\n",
    "The following figure shows a simple example of a neural network with two hidden\n",
    "layers, an input size of two and an output size of three. Each edge between\n",
    "neurons has a corresponding weight that is learnable in the training phase.\n",
    "\n",
    "```{image} neural_network.png\n",
    "\n",
    "```\n",
    "\n",
    "To train the neural network, one considers a so called \"loss function\", that\n",
    "measures how the neural network performs on the training set {math}`S`, i.e.\n",
    "how accurately the neural network reproduces the output {math}`h(\\mu_i)` given\n",
    "the input {math}`\\mu_i`. The weights of the neural network are adjusted\n",
    "iteratively such that the loss function is successively minimized. To this end,\n",
    "one typically uses a Quasi-Newton method for small neural networks or a\n",
    "(stochastic) gradient descent method for deep neural networks (those with many\n",
    "hidden layers).\n",
    "\n",
    "A possibility to use feedforward neural networks in combination with reduced\n",
    "basis methods will be introduced in the following section.\n",
    "\n",
    "## A non-intrusive reduced order method using artificial neural networks\n",
    "\n",
    "We now assume that we are given a parametric pyMOR {{ Model }} for which we want\n",
    "to compute a reduced order surrogate {{ Model }} using a neural network. In this\n",
    "example, we consider the following two-dimensional diffusion problem with\n",
    "parametrized diffusion, right hand side and Dirichlet boundary condition:\n",
    "\n",
    "```{math}\n",
    "-\\nabla \\cdot \\big(\\sigma(x, \\mu) \\nabla u(x, \\mu) \\big) = f(x, \\mu),\\quad x=(x_1,x_2) \\in \\Omega,\n",
    "```\n",
    "\n",
    "on the domain {math}`\\Omega:= (0, 1)^2 \\subset \\mathbb{R}^2` with data\n",
    "functions {math}`f((x_1, x_2), \\mu) = 10 \\cdot \\mu + 0.1`,\n",
    "{math}`\\sigma((x_1, x_2), \\mu) = (1 - x_1) \\cdot \\mu + x_1`, where\n",
    "{math}`\\mu \\in (0.1, 1)` denotes the parameter. Further, we apply the\n",
    "Dirichlet boundary conditions\n",
    "\n",
    "```{math}\n",
    "u((x_1, x_2), \\mu) = 2x_1\\mu + 0.5,\\quad x=(x_1, x_2) \\in \\partial\\Omega.\n",
    "```\n",
    "\n",
    "We discretize the problem using pyMOR's builtin discretization toolkit as\n",
    "explained in {doc}`tutorial_builtin_discretizer`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d39d09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd75cf30255d4bb096040cdaad2769ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymor.basic import *\n",
    "\n",
    "problem = StationaryProblem(\n",
    "      domain=RectDomain(),\n",
    "\n",
    "      rhs=LincombFunction(\n",
    "          [ExpressionFunction('10', 2), ConstantFunction(1., 2)],\n",
    "          [ProjectionParameterFunctional('mu'), 0.1]),\n",
    "\n",
    "      diffusion=LincombFunction(\n",
    "          [ExpressionFunction('1 - x[0]', 2), ExpressionFunction('x[0]', 2)],\n",
    "          [ProjectionParameterFunctional('mu'), 1]),\n",
    "\n",
    "      dirichlet_data=LincombFunction(\n",
    "          [ExpressionFunction('2 * x[0]', 2), ConstantFunction(1., 2)],\n",
    "          [ProjectionParameterFunctional('mu'), 0.5]),\n",
    "\n",
    "      name='2DProblem'\n",
    "  )\n",
    "\n",
    "fom, _ = discretize_stationary_cg(problem, diameter=1/50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a2549c",
   "metadata": {},
   "source": [
    "Since we employ a single {{ Parameter }}, and thus use the same range for each\n",
    "parameter, we can create the {{ ParameterSpace }} using the following line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ccef1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_space = fom.parameters.space((0.1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a468898",
   "metadata": {},
   "source": [
    "The main idea of the approach by Hesthaven et al. is to approximate the mapping\n",
    "from the {{ Parameters }} to the coefficients of the respective solution in a\n",
    "reduced basis by means of a neural network. Thus, in the online phase, one\n",
    "performs a forward pass of the {{ Parameters }} through the neural networks and\n",
    "obtains the approximated reduced coordinates. To derive the corresponding\n",
    "high-fidelity solution, one can further use the reduced basis and compute the\n",
    "linear combination defined by the reduced coefficients. The reduced basis is\n",
    "created via POD.\n",
    "\n",
    "The method described above is \"non-intrusive\", which means that no deep insight\n",
    "into the model or its implementation is required and it is completely\n",
    "sufficient to be able to generate full order snapshots for a randomly chosen\n",
    "set of parameters. This is one of the main advantages of the proposed approach,\n",
    "since one can simply train a neural network, check its performance and resort\n",
    "to a different method if the neural network does not provide proper\n",
    "approximation results.\n",
    "\n",
    "In pyMOR, there exists a training routine for feedforward neural networks. This\n",
    "procedure is part of a reductor and it is not necessary to write a custom\n",
    "training algorithm for each specific problem. However, it is sometimes\n",
    "necessary to try different architectures for the neural network to find the one\n",
    "that best fits the problem at hand. In the reductor, one can easily adjust the\n",
    "number of layers and the number of neurons in each hidden layer, for instance.\n",
    "Furthermore, it is also possible to change the deployed activation function.\n",
    "\n",
    "To train the neural network, we create a training and a validation set\n",
    "consisting of 100 and 20 randomly chosen {{ parameter_values }}, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5d88f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "  training_set = parameter_space.sample_uniformly(100)\n",
    "  validation_set = parameter_space.sample_randomly(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18178cc",
   "metadata": {},
   "source": [
    "In this tutorial, we construct the reduced basis such that no more modes than\n",
    "required to bound the l2-approximation error by a given value are used.\n",
    "The l2-approximation error is  the error of the orthogonal projection (in the\n",
    "l2-sense) of the training snapshots onto the reduced basis. That is, we\n",
    "prescribe `l2_err` in the reductor. It is also possible to determine a relative\n",
    "or absolute tolerance (in the singular values) that should not be exceeded on\n",
    "the training set. Further, one can preset the size of the reduced basis.\n",
    "\n",
    "The training is aborted when a neural network that guarantees our prescribed\n",
    "tolerance is found. If we set `ann_mse` to `None`, this function will\n",
    "automatically train several neural networks with different initial weights and\n",
    "select the one leading to the best results on the validation set. We can also\n",
    "set `ann_mse` to `'like_basis'`. Then, the algorithm tries to train a neural\n",
    "network that leads to a mean squared error on the training set that is as small\n",
    "as the error of the reduced basis. If the maximal number of restarts is reached\n",
    "without finding a network that fulfills the tolerances, an exception is raised.\n",
    "In such a case, one could try to change the architecture of the neural network\n",
    "or switch to `ann_mse=None` which is guaranteed to produce a reduced order\n",
    "model (perhaps with insufficient approximation properties).\n",
    "\n",
    "We can now construct a reductor with prescribed error for the basis and mean\n",
    "squared error of the neural network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c118df3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymor.reductors.neural_network import NeuralNetworkReductor\n",
    "\n",
    "reductor = NeuralNetworkReductor(fom,\n",
    "                                 training_set,\n",
    "                                 validation_set,\n",
    "                                 l2_err=1e-5,\n",
    "                                 ann_mse=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266db70b",
   "metadata": {},
   "source": [
    "To reduce the model, i.e. compute a reduced basis via POD and train the neural\n",
    "network, we use the respective function of the\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkReductor`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "876cb98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c99e67db061410c939718b16f1dfa4f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rom = reductor.reduce(restarts=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b18340",
   "metadata": {},
   "source": [
    "We are now ready to test our reduced model by solving for a random parameter value\n",
    "the full problem and the reduced model and visualize the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32bc4d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c380a03ec774c98abb0ee19cc84e722",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88be6103aa4647caa35befc0fc9e5133",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = parameter_space.sample_randomly()\n",
    "\n",
    "U = fom.solve(mu)\n",
    "U_red = rom.solve(mu)\n",
    "U_red_recon = reductor.reconstruct(U_red)\n",
    "\n",
    "fom.visualize((U, U_red_recon),\n",
    "              legend=(f'Full solution for parameter {mu}', f'Reduced solution for parameter {mu}'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb3bc46",
   "metadata": {},
   "source": [
    "Finally, we measure the error of our neural network and the performance\n",
    "compared to the solution of the full order problem on a training set. To this\n",
    "end, we sample randomly some {{ parameter_values }} from our {{ ParameterSpace }}:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04e8d6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = parameter_space.sample_randomly(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1392c8d",
   "metadata": {},
   "source": [
    "Next, we create empty solution arrays for the full and reduced solutions and an\n",
    "empty list for the speedups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f278bfb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "U = fom.solution_space.empty(reserve=len(test_set))\n",
    "U_red = fom.solution_space.empty(reserve=len(test_set))\n",
    "\n",
    "speedups = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d085bf37",
   "metadata": {},
   "source": [
    "Now, we iterate over the test set, compute full and reduced solutions to the\n",
    "respective parameters and measure the speedup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6685b5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa840a7851ea49fc9b630eb3db6da6f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "for mu in test_set:\n",
    "    tic = time.perf_counter()\n",
    "    U.append(fom.solve(mu))\n",
    "    time_fom = time.perf_counter() - tic\n",
    "\n",
    "    tic = time.perf_counter()\n",
    "    U_red.append(reductor.reconstruct(rom.solve(mu)))\n",
    "    time_red = time.perf_counter() - tic\n",
    "\n",
    "    speedups.append(time_fom / time_red)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "814dfd63",
   "metadata": {},
   "source": [
    "We can now derive the absolute and relative errors on the training set as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "511f1e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "absolute_errors = (U - U_red).norm()\n",
    "relative_errors = (U - U_red).norm() / U.norm()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27086487",
   "metadata": {},
   "source": [
    "The average absolute error amounts to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d791b6b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0030704336433911355"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.average(absolute_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89d0de6",
   "metadata": {},
   "source": [
    "On the other hand, the average relative error is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5775398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.152392212077549e-05"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(relative_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9cede2e",
   "metadata": {},
   "source": [
    "Using neural networks results in the following median speedup compared to\n",
    "solving the full order problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf44e3b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.074253461575818"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(speedups)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d1e664d",
   "metadata": {},
   "source": [
    "Since {class}`~pymor.reductors.neural_network.NeuralNetworkReductor` only calls\n",
    "the {meth}`~pymor.models.interface.Model.solve` method of the {{ Model }}, it can easily\n",
    "be applied to {{ Models }} originating from external solvers, without requiring any access to\n",
    "{{ Operators }} internal to the solver.\n",
    "\n",
    "Direct approximation of output quantities\n",
    "-----------------------------------------\n",
    "\n",
    "Thus far, we were mainly interested in approximating the solution state\n",
    "{math}`u(\\mu)\\equiv u(\\cdot,\\mu)` for some parameter {math}`\\mu`. If we consider an output\n",
    "functional {math}`\\mathcal{J}(\\mu):= J(u(\\mu), \\mu)`, one can use the reduced solution\n",
    "{math}`u_N(\\mu)` for computing the output as {math}`\\mathcal{J}(\\mu)\\approx J(u_N(\\mu),\\mu)`.\n",
    "However, when dealing with neural networks, one could also think about directly learning the\n",
    "mapping from parameter to output. That is, one can use a neural network to approximate\n",
    "{math}`\\mathcal{J}\\colon\\mathcal{P}\\to\\mathbb{R}^q`, where {math}`q\\in\\mathbb{N}` denotes\n",
    "the output dimension.\n",
    "\n",
    "In the following, we will extend our problem from the last section by an output functional\n",
    "and use the {class}`~pymor.reductors.neural_network.NeuralNetworkStatefreeOutputReductor` to\n",
    "derive a reduced model that can solely be used to solve for the output quantity without\n",
    "computing a reduced state at all.\n",
    "\n",
    "For the definition of the output, we define the output of out problem as the l2-product of the\n",
    "solution with the right hand side respectively Dirichlet boundary data of our original problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c099e81b",
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = problem.with_(outputs=[('l2', problem.rhs), ('l2_boundary', problem.dirichlet_data)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3007fa2b",
   "metadata": {},
   "source": [
    "Consequently, the output dimension is {math}`q=2`. After adjusting the problem definition,\n",
    "we also have to update the full order model to be aware of the output quantities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3b6db6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a2c3303a4824c33bf7d07a77952b99e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fom, _ = discretize_stationary_cg(problem, diameter=1/50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57a4f3b",
   "metadata": {},
   "source": [
    "We can now import the {class}`~pymor.reductors.neural_network.NeuralNetworkStatefreeOutputReductor`\n",
    "and initialize the reductor using the same data as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eb7b3a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymor.reductors.neural_network import NeuralNetworkStatefreeOutputReductor\n",
    "\n",
    "output_reductor = NeuralNetworkStatefreeOutputReductor(fom,\n",
    "                                                       training_set,\n",
    "                                                       validation_set,\n",
    "                                                       validation_loss=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07bbe6a1",
   "metadata": {},
   "source": [
    "Similar to the `NeuralNetworkReductor`, we can call `reduce` to obtain a reduced order model.\n",
    "In this case, `reduce` trains a neural network to approximate the mapping from parameter to\n",
    "output directly. Therefore, we can only use the resulting reductor to solve for the outputs\n",
    "and not for state approximations. The `NeuralNetworkReductor` though can be used to do both by\n",
    "calling `solve` respectively `output` (if we had initialized the `NeuralNetworkReductor` with\n",
    "the problem including the output quantities).\n",
    "\n",
    "We now perform the reduction and run some tests with the resulting\n",
    "{class}`~pymor.models.neural_network.NeuralNetworkStatefreeOutputModel`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "96db9e16",
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "output_rom = output_reductor.reduce(restarts=100)\n",
    "\n",
    "outputs = []\n",
    "outputs_red = []\n",
    "outputs_speedups = []\n",
    "\n",
    "for mu in test_set:\n",
    "    tic = time.perf_counter()\n",
    "    outputs.append(fom.output(mu=mu))\n",
    "    time_fom = time.perf_counter() - tic\n",
    "\n",
    "    tic = time.perf_counter()\n",
    "    outputs_red.append(output_rom.output(mu=mu))\n",
    "    time_red = time.perf_counter() - tic\n",
    "\n",
    "    outputs_speedups.append(time_fom / time_red)\n",
    "\n",
    "outputs = np.squeeze(np.array(outputs))\n",
    "outputs_red = np.squeeze(np.array(outputs_red))\n",
    "\n",
    "outputs_absolute_errors = np.abs(outputs - outputs_red)\n",
    "outputs_relative_errors = np.abs(outputs - outputs_red) / np.abs(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e20b0800",
   "metadata": {},
   "source": [
    "The average absolute error (component-wise) on the training set is given by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aaed9806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00044459265908829425"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(outputs_absolute_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a406b7",
   "metadata": {},
   "source": [
    "The average relative error is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a68e41a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0001589414267216673"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(outputs_relative_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ee17eb",
   "metadata": {},
   "source": [
    "and the median of the speedups amounts to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2bab3283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.419331185528836"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(outputs_speedups)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01eb2d80",
   "metadata": {},
   "source": [
    "Neural networks for instationary problems\n",
    "-----------------------------------------\n",
    "\n",
    "To solve instationary problems using neural networks, we have extended the\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkReductor` to the\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkInstationaryReductor`, which treats time\n",
    "as an additional parameter (see {cite}`WHR19`). The resulting\n",
    "{class}`~pymor.models.neural_network.NeuralNetworkInstationaryModel` passes the input, together\n",
    "with the current time instance, through the neural network in each time step to obtain reduced\n",
    "coefficients. In the same fashion, there exists a\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkInstationaryStatefreeOutputReductor` and the\n",
    "corresponding {class}`~pymor.models.neural_network.NeuralNetworkInstationaryStatefreeOutputModel`.\n",
    "\n",
    "Download the code:\n",
    "{download}`tutorial_mor_with_anns.md`\n",
    "{nb-download}`tutorial_mor_with_anns.ipynb`"
   ]
  }
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\n",
          "text/plain": "<Figure size 640x480 with 1 Axes>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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<bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.15454545454545454]} ...</p><p style=\"line-height:120%\">01:24 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.16363636363636364]} ...</p><p style=\"line-height:120%\">01:24 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.17272727272727273]} ...</p><p style=\"line-height:120%\">01:24 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.18181818181818182]} ...</p><p style=\"line-height:120%\">01:24 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.19090909090909092]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2090909090909091]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2181818181818182]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.22727272727272727]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.23636363636363636]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.24545454545454545]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2545454545454545]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.26363636363636367]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2727272727272727]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.28181818181818186]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2909090909090909]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3090909090909091]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3181818181818182]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.32727272727272727]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.33636363636363636]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.34545454545454546]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3545454545454545]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.36363636363636365]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3727272727272727]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.38181818181818183]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3909090909090909]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.40909090909090906]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4181818181818182]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.42727272727272725]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4363636363636364]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.44545454545454544]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4545454545454546]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4636363636363636]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.47272727272727266]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4818181818181818]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.49090909090909085]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.509090909090909]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5181818181818182]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5272727272727272]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5363636363636364]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5454545454545454]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5545454545454546]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5636363636363636]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5727272727272728]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5818181818181818]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5909090909090909]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.609090909090909]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6181818181818182]} ...</p><p style=\"line-height:120%\">01:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6272727272727272]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6363636363636364]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6454545454545454]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6545454545454545]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6636363636363636]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6727272727272727]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6818181818181818]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6909090909090908]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.709090909090909]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7181818181818181]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7272727272727272]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7363636363636363]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7454545454545454]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7545454545454545]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7636363636363636]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7727272727272727]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7818181818181817]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7909090909090909]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7999999999999999]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8090909090909091]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8181818181818181]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8272727272727273]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8363636363636363]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8454545454545453]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8545454545454545]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8636363636363635]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8727272727272727]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8818181818181817]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8909090909090909]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8999999999999999]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9090909090909091]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9181818181818181]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9272727272727272]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9363636363636363]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9454545454545454]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9545454545454545]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9636363636363636]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9727272727272727]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9818181818181817]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9909090909090909]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [1.0]} ...</p><p style=\"line-height:120%\">01:26 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Computing validation snapshots ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4370861069626263]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9556428757689246]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7587945476302645]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6387926357773329]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.24041677639819287]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2403950683025824]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.15227525095137953]} ...</p><p style=\"line-height:120%\">01:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8795585311974417]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6410035105688879]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.737265320016441]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.1185260448662222]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9729188669457949]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8491983767203796]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.29110519961044856]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.26364247048639056]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2650640588680905]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.373818018663584]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5722807884690141]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.48875051677790415]} ...</p><p style=\"line-height:120%\">01:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.36210622617823773]} ...</p><p style=\"line-height:120%\">01:27 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Training of neural network ...</p><p style=\"line-height:120%\">01:27 |   <bold>NeuralNetworkStatefreeOutputReductor</bold>: Initializing neural network ...</p><p style=\"line-height:120%\">01:27 |   <bold>FullyConnectedNN</bold>: Architecture of the neural network:\nFullyConnectedNN(\n  (layers): ModuleList(\n    (0): Linear(in_features=1, out_features=9, bias=True)\n    (1): Linear(in_features=9, out_features=9, bias=True)\n    (2): Linear(in_features=9, out_features=2, bias=True)\n  )\n)</p><p style=\"line-height:120%\">01:27 |   <bold>multiple_restarts_training</bold>: Performing up to 100 restarts to train a neural network with a loss below 1.000e-05 ...</p><p style=\"line-height:120%\">01:27 |   <bold>multiple_restarts_training</bold>: Training neural network #0 ...</p><p style=\"line-height:120%\">01:27 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:28 |   |   <bold>train_neural_network</bold>: Stopping training process early after 31 epochs with validation loss of 2.997e-07 ...</p><p style=\"line-height:120%\">01:28 |   <bold>multiple_restarts_training</bold>: Finished training after 0 restarts, found neural network with loss of 4.006e-07 ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Using neural network with smallest validation error ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Finished training with a validation loss of 2.997209461376144e-07 ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.22554447458683766]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.22554447458683766]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3629301836816964]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.3629301836816964]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4297256589643226]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.4297256589643226]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5104629857953323]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.5104629857953323]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8066583652537123]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.8066583652537123]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2797064039425238]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.2797064039425238]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5628109945722505]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.5628109945722505]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6331731119758383]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.6331731119758383]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.14180537144799796]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.14180537144799796]} ...</p><p style=\"line-height:120%\">01:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6467903667112945]} ...</p><p style=\"line-height:120%\">01:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.6467903667112945]} ...</p>"
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       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "1.5.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "1.5.0",
       "_view_name": "HTMLView",
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       "value": "<p style=\"line-height:120%\">01:23 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6506676052501416]} ...</p><p style=\"line-height:120%\">01:23 <bold>NeuralNetworkModel</bold>: Solving 2DProblem_CG_reduced for {input: , mu: [0.6506676052501416]} ...</p>"
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     "a05188c4b28c494a885f95cee9586ea2": {
      "model_module": "@jupyter-widgets/controls",
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       "_model_module_version": "1.5.0",
       "_model_name": "DescriptionStyleModel",
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       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
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      "state": {
       "_model_module": "@jupyter-widgets/base",
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       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
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       "value": "<p style=\"line-height:120%\">00:02 <bold>NeuralNetworkReductor</bold>: Computing training snapshots ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.1]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.1090909090909091]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.1181818181818182]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.1272727272727273]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.13636363636363635]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.14545454545454545]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.15454545454545454]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.16363636363636364]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.17272727272727273]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.18181818181818182]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.19090909090909092]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2090909090909091]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2181818181818182]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.22727272727272727]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.23636363636363636]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.24545454545454545]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2545454545454545]} ...</p><p style=\"line-height:120%\">00:02 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.26363636363636367]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2727272727272727]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.28181818181818186]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2909090909090909]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3090909090909091]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3181818181818182]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.32727272727272727]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.33636363636363636]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.34545454545454546]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3545454545454545]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.36363636363636365]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3727272727272727]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.38181818181818183]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3909090909090909]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.40909090909090906]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4181818181818182]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.42727272727272725]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4363636363636364]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.44545454545454544]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4545454545454546]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4636363636363636]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.47272727272727266]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4818181818181818]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.49090909090909085]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.509090909090909]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5181818181818182]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5272727272727272]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5363636363636364]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5454545454545454]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5545454545454546]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5636363636363636]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5727272727272728]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5818181818181818]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5909090909090909]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.609090909090909]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6181818181818182]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6272727272727272]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6363636363636364]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6454545454545454]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6545454545454545]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6636363636363636]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6727272727272727]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6818181818181818]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6909090909090908]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.709090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7181818181818181]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7272727272727272]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7363636363636363]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7454545454545454]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7545454545454545]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7636363636363636]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7727272727272727]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7818181818181817]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7909090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7999999999999999]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8090909090909091]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8181818181818181]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8272727272727273]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8363636363636363]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8454545454545453]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8545454545454545]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8636363636363635]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8727272727272727]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8818181818181817]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8909090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8999999999999999]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9090909090909091]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9181818181818181]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9272727272727272]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9363636363636363]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9454545454545454]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9545454545454545]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9636363636363636]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9727272727272727]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9818181818181817]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9909090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [1.0]} ...</p><p style=\"line-height:120%\">00:04 <bold>NeuralNetworkReductor</bold>: Building reduced basis ...</p><p style=\"line-height:120%\">00:04 |   <bold>pod</bold>: Computing SVD ...</p><p style=\"line-height:120%\">00:04 |   |   <bold>method_of_snapshots</bold>: Computing Gramian (100 vectors) ...</p><p style=\"line-height:120%\">00:04 |   |   <bold>method_of_snapshots</bold>: Computing eigenvalue decomposition ...</p><p style=\"line-height:120%\">00:04 |   |   <bold>method_of_snapshots</bold>: Computing left-singular vectors (8 vectors) ...</p><p style=\"line-height:120%\">00:04 |   <bold>pod</bold>: Checking orthonormality ...</p><p style=\"line-height:120%\">00:04 |   <bold>pod</bold>: Reorthogonalizing POD modes ...</p><p style=\"line-height:120%\">00:04 <bold>NeuralNetworkReductor</bold>: Computing training samples ...</p><p style=\"line-height:120%\">00:04 <bold>NeuralNetworkReductor</bold>: Computing validation snapshots ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4370861069626263]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9556428757689246]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7587945476302645]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6387926357773329]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.24041677639819287]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2403950683025824]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.15227525095137953]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8795585311974417]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6410035105688879]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.737265320016441]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.1185260448662222]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9729188669457949]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8491983767203796]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.29110519961044856]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.26364247048639056]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2650640588680905]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.373818018663584]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5722807884690141]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.48875051677790415]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.36210622617823773]} ...</p><p style=\"line-height:120%\">00:05 <bold>NeuralNetworkReductor</bold>: Training of neural network ...</p><p style=\"line-height:120%\">00:05 |   <bold>NeuralNetworkReductor</bold>: Initializing neural network ...</p><p style=\"line-height:120%\">00:05 |   <bold>FullyConnectedNN</bold>: Architecture of the neural network:\nFullyConnectedNN(\n  (layers): ModuleList(\n    (0): Linear(in_features=1, out_features=27, bias=True)\n    (1): Linear(in_features=27, out_features=27, bias=True)\n    (2): Linear(in_features=27, out_features=8, bias=True)\n  )\n)</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Performing up to 100 restarts to train a neural network with a loss below 1.000e-05 ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Training neural network #0 ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 9.254e+01 ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Training neural network #1 ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 9.491e+01 ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 8.830e+01) ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Training neural network #2 ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Stopping training process early after 12 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 8.830e+01) ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Training neural network #3 ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 1.806e+01 ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Found better neural network (loss of 1.918e+01 instead of 8.830e+01) ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Training neural network #4 ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Stopping training process early after 12 epochs with validation loss of 4.018e+00 ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Found better neural network (loss of 3.729e+00 instead of 1.918e+01) ...</p><p style=\"line-height:120%\">00:05 |   <bold>multiple_restarts_training</bold>: Training neural network #5 ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:08 |   |   <bold>train_neural_network</bold>: Stopping training process early after 48 epochs with validation loss of 9.830e-06 ...</p><p style=\"line-height:120%\">00:08 |   <bold>multiple_restarts_training</bold>: Found better neural network (loss of 1.352e-05 instead of 3.729e+00) ...</p><p style=\"line-height:120%\">00:08 |   <bold>multiple_restarts_training</bold>: Training neural network #6 ...</p><p style=\"line-height:120%\">00:08 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:16 |   |   <bold>train_neural_network</bold>: Stopping training process early after 410 epochs with validation loss of 5.201e-05 ...</p><p style=\"line-height:120%\">00:16 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 7.753e-05 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:16 |   <bold>multiple_restarts_training</bold>: Training neural network #7 ...</p><p style=\"line-height:120%\">00:16 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:16 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">00:16 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:16 |   <bold>multiple_restarts_training</bold>: Training neural network #8 ...</p><p style=\"line-height:120%\">00:16 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:21 |   |   <bold>train_neural_network</bold>: Stopping training process early after 85 epochs with validation loss of 2.541e-04 ...</p><p style=\"line-height:120%\">00:21 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 2.724e-04 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:21 |   <bold>multiple_restarts_training</bold>: Training neural network #9 ...</p><p style=\"line-height:120%\">00:21 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:21 |   |   <bold>train_neural_network</bold>: Stopping training process early after 18 epochs with validation loss of 9.491e+01 ...</p><p style=\"line-height:120%\">00:21 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:21 |   <bold>multiple_restarts_training</bold>: Training neural network #10 ...</p><p style=\"line-height:120%\">00:21 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:21 |   |   <bold>train_neural_network</bold>: Stopping training process early after 15 epochs with validation loss of 1.953e+01 ...</p><p style=\"line-height:120%\">00:21 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 1.990e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:21 |   <bold>multiple_restarts_training</bold>: Training neural network #11 ...</p><p style=\"line-height:120%\">00:21 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>train_neural_network</bold>: Stopping training process early after 419 epochs with validation loss of 4.279e-04 ...</p><p style=\"line-height:120%\">00:29 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 4.536e-04 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:29 |   <bold>multiple_restarts_training</bold>: Training neural network #12 ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>train_neural_network</bold>: Stopping training process early after 12 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">00:29 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:29 |   <bold>multiple_restarts_training</bold>: Training neural network #13 ...</p><p style=\"line-height:120%\">00:29 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:30 |   |   <bold>train_neural_network</bold>: Stopping training process early after 13 epochs with validation loss of 9.491e+01 ...</p><p style=\"line-height:120%\">00:30 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:30 |   <bold>multiple_restarts_training</bold>: Training neural network #14 ...</p><p style=\"line-height:120%\">00:30 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:30 |   |   <bold>train_neural_network</bold>: Stopping training process early after 12 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">00:30 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:30 |   <bold>multiple_restarts_training</bold>: Training neural network #15 ...</p><p style=\"line-height:120%\">00:30 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:31 |   |   <bold>train_neural_network</bold>: Stopping training process early after 40 epochs with validation loss of 1.061e-01 ...</p><p style=\"line-height:120%\">00:31 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 1.118e-01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:31 |   <bold>multiple_restarts_training</bold>: Training neural network #16 ...</p><p style=\"line-height:120%\">00:31 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:32 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 9.483e+01 ...</p><p style=\"line-height:120%\">00:32 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.909e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:32 |   <bold>multiple_restarts_training</bold>: Training neural network #17 ...</p><p style=\"line-height:120%\">00:32 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:32 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">00:32 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:32 |   <bold>multiple_restarts_training</bold>: Training neural network #18 ...</p><p style=\"line-height:120%\">00:32 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:32 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 6.109e+01 ...</p><p style=\"line-height:120%\">00:32 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 5.903e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:32 |   <bold>multiple_restarts_training</bold>: Training neural network #19 ...</p><p style=\"line-height:120%\">00:32 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:35 |   |   <bold>train_neural_network</bold>: Stopping training process early after 62 epochs with validation loss of 5.701e-05 ...</p><p style=\"line-height:120%\">00:35 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 5.966e-05 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:35 |   <bold>multiple_restarts_training</bold>: Training neural network #20 ...</p><p style=\"line-height:120%\">00:35 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:35 |   |   <bold>train_neural_network</bold>: Stopping training process early after 14 epochs with validation loss of 2.862e+01 ...</p><p style=\"line-height:120%\">00:35 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 2.690e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:35 |   <bold>multiple_restarts_training</bold>: Training neural network #21 ...</p><p style=\"line-height:120%\">00:35 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:36 |   |   <bold>train_neural_network</bold>: Stopping training process early after 12 epochs with validation loss of 5.058e+01 ...</p><p style=\"line-height:120%\">00:36 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 4.736e+01 (instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:36 |   <bold>multiple_restarts_training</bold>: Training neural network #22 ...</p><p style=\"line-height:120%\">00:36 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:42 |   |   <bold>train_neural_network</bold>: Stopping training process early after 375 epochs with validation loss of 1.327e-05 ...</p><p style=\"line-height:120%\">00:42 |   <bold>multiple_restarts_training</bold>: Found better neural network (loss of 1.345e-05 instead of 1.352e-05) ...</p><p style=\"line-height:120%\">00:42 |   <bold>multiple_restarts_training</bold>: Training neural network #23 ...</p><p style=\"line-height:120%\">00:42 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:42 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 3.277e+01 ...</p><p style=\"line-height:120%\">00:42 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 3.295e+01 (instead of 1.345e-05) ...</p><p style=\"line-height:120%\">00:42 |   <bold>multiple_restarts_training</bold>: Training neural network #24 ...</p><p style=\"line-height:120%\">00:42 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:44 |   |   <bold>train_neural_network</bold>: Stopping training process early after 274 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">00:44 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.345e-05) ...</p><p style=\"line-height:120%\">00:44 |   <bold>multiple_restarts_training</bold>: Training neural network #25 ...</p><p style=\"line-height:120%\">00:44 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:50 |   |   <bold>train_neural_network</bold>: Stopping training process early after 355 epochs with validation loss of 1.728e-04 ...</p><p style=\"line-height:120%\">00:50 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 1.860e-04 (instead of 1.345e-05) ...</p><p style=\"line-height:120%\">00:50 |   <bold>multiple_restarts_training</bold>: Training neural network #26 ...</p><p style=\"line-height:120%\">00:50 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:50 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 6.394e+01 ...</p><p style=\"line-height:120%\">00:50 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 5.814e+01 (instead of 1.345e-05) ...</p><p style=\"line-height:120%\">00:50 |   <bold>multiple_restarts_training</bold>: Training neural network #27 ...</p><p style=\"line-height:120%\">00:50 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:51 |   |   <bold>train_neural_network</bold>: Stopping training process early after 12 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">00:51 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.345e-05) ...</p><p style=\"line-height:120%\">00:51 |   <bold>multiple_restarts_training</bold>: Training neural network #28 ...</p><p style=\"line-height:120%\">00:51 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:57 |   |   <bold>train_neural_network</bold>: Stopping training process early after 388 epochs with validation loss of 9.503e-06 ...</p><p style=\"line-height:120%\">00:57 |   <bold>multiple_restarts_training</bold>: Found better neural network (loss of 1.113e-05 instead of 1.345e-05) ...</p><p style=\"line-height:120%\">00:57 |   <bold>multiple_restarts_training</bold>: Training neural network #29 ...</p><p style=\"line-height:120%\">00:57 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:57 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 9.476e+01 ...</p><p style=\"line-height:120%\">00:57 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.906e+01 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">00:57 |   <bold>multiple_restarts_training</bold>: Training neural network #30 ...</p><p style=\"line-height:120%\">00:57 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:57 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">00:57 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">00:57 |   <bold>multiple_restarts_training</bold>: Training neural network #31 ...</p><p style=\"line-height:120%\">00:57 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:05 |   |   <bold>train_neural_network</bold>: Stopping training process early after 402 epochs with validation loss of 1.144e-04 ...</p><p style=\"line-height:120%\">01:05 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 1.266e-04 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:05 |   <bold>multiple_restarts_training</bold>: Training neural network #32 ...</p><p style=\"line-height:120%\">01:05 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:09 |   |   <bold>train_neural_network</bold>: Stopping training process early after 72 epochs with validation loss of 2.440e-05 ...</p><p style=\"line-height:120%\">01:09 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 2.975e-05 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:09 |   <bold>multiple_restarts_training</bold>: Training neural network #33 ...</p><p style=\"line-height:120%\">01:09 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:10 |   |   <bold>train_neural_network</bold>: Stopping training process early after 15 epochs with validation loss of 2.403e+01 ...</p><p style=\"line-height:120%\">01:10 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 2.733e+01 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:10 |   <bold>multiple_restarts_training</bold>: Training neural network #34 ...</p><p style=\"line-height:120%\">01:10 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:10 |   |   <bold>train_neural_network</bold>: Stopping training process early after 12 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">01:10 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:10 |   <bold>multiple_restarts_training</bold>: Training neural network #35 ...</p><p style=\"line-height:120%\">01:10 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:10 |   |   <bold>train_neural_network</bold>: Stopping training process early after 12 epochs with validation loss of 1.751e+01 ...</p><p style=\"line-height:120%\">01:10 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 1.805e+01 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:10 |   <bold>multiple_restarts_training</bold>: Training neural network #36 ...</p><p style=\"line-height:120%\">01:10 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:18 |   |   <bold>train_neural_network</bold>: Stopping training process early after 386 epochs with validation loss of 4.097e-04 ...</p><p style=\"line-height:120%\">01:18 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 4.076e-04 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:18 |   <bold>multiple_restarts_training</bold>: Training neural network #37 ...</p><p style=\"line-height:120%\">01:18 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:18 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 7.388e+01 ...</p><p style=\"line-height:120%\">01:18 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 7.011e+01 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:18 |   <bold>multiple_restarts_training</bold>: Training neural network #38 ...</p><p style=\"line-height:120%\">01:18 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:18 |   |   <bold>train_neural_network</bold>: Stopping training process early after 11 epochs with validation loss of 9.492e+01 ...</p><p style=\"line-height:120%\">01:18 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.914e+01 (instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:18 |   <bold>multiple_restarts_training</bold>: Training neural network #39 ...</p><p style=\"line-height:120%\">01:18 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">01:23 |   |   <bold>train_neural_network</bold>: Stopping training process early after 344 epochs with validation loss of 1.965e-06 ...</p><p style=\"line-height:120%\">01:23 |   <bold>multiple_restarts_training</bold>: Found better neural network (loss of 1.684e-06 instead of 1.113e-05) ...</p><p style=\"line-height:120%\">01:23 |   <bold>multiple_restarts_training</bold>: Finished training after 39 restarts, found neural network with loss of 1.684e-06 ...</p><p style=\"line-height:120%\">01:23 <bold>NeuralNetworkReductor</bold>: Checking tolerances for error of neural network ...</p><p style=\"line-height:120%\">01:23 <bold>NeuralNetworkReductor</bold>: Building ROM ...</p>"
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